Figure REF  compares different models in terms of the factors described above. First, all models improve the average intelligibility score over the unprocessed mixture. We notice some subjects gave high intelligibility scores to some of the unprocessed signals even when they had low SNRs. We think this is because humans can attend to a source in the presence of multiple distracting stimuli thanks to the cocktail party effect [1]}, thus they may focus on and exert themselves to understand the target speech. But overall, the strong background noise makes the speech much less intelligible. While the MCSDN has a high score for noise reduction due to the power of nonlinear models, it can cause speech distortion. If the mixture is too noisy, the model may also filter out speech components when it removes the noise. Therefore, the MCSDN only provides a slight intelligibility improvement and poor listening improvement.
